# Quantitative features with HeterogeneityCad **URL:** \ **Category:** Support\ **Tags:** radiomics\ **Created:** [June 16, 2017, 10:47am UTC](https://discourse.slicer.org/t/quantitative-features-with-heterogeneitycad/518 "2017-06-16T10:47:15Z")\ **Posts on this page:** 1\ **Showing post:** 23

**Author:** ![JoostJM](https://sea2.discourse-cdn.com/flex002/user_avatar/discourse.slicer.org/joostjm/32/1091_2.png) [@JoostJM](https://discourse.slicer.org/u/JoostJM)\ **Post date:** [June 23, 2017, 7:35pm UTC](https://discourse.slicer.org/t/quantitative-features-with-heterogeneitycad/518/23 "2017-06-23T19:35:56Z")

Hi Matteo, As @fedorov mentioned, I compared the output of PyRadiomics to a digitial phantom, but I’m not entirely sure this is the same as Andriy mentioned. It is the digital phantom used in the article with the feature definitions Andriy sent (The IBSI document). That being said, I think the differences you’re experiencing may be in part explained by @jayender’s comment, namely the discretization. For PyRadiomics, this is determined by a fixed bin width (as opposed to a fixed bin count) with a default value of 25 units. I’m not sure about how this is implemented in HeterogeneityCAD or what default settings are. More information on the exact method implemented in PyRadiomics can be found [here](http://pyradiomics.readthedocs.io/en/latest/radiomics.html#radiomics.imageoperations.binImage). However, this only affects Entropy and Uniformity. The exact cause of the difference in Kurtosis I do not know, but it strikes me that the difference is _exactly_ 3. @jayender, does the HeterogeneityCAD system implement a fixed offset? This would be a fixed value of 3, which centers kurtosis on 0 for normal distributions (PyRadiomics does not have this offset, which would explain the difference). In fact, this offset is also implemented in the IBSI document. Cheers, Joost --- _[View the full topic](https://discourse.slicer.org/t/quantitative-features-with-heterogeneitycad/518)._